In this paper, a region-determined localization method applied for unmanned ground vehicles (UGVs) is presented. The method aims to solve GNSS-denied localization problem using pole-like feature such as trees or street lights. The approach includes three parts: mapping, bounding, and localization. To map and reconstruct the environment, the hector mapping approach and circle-fitting method are adopted for the occupancy mapping and feature mapping. To bound out the available working region, we define the intersection area of features' enlarged radius and desired operating area as negative and positive virtual boundaries. While the robot is cruising, the likelihood detection method is adopted for obstacle searching and comparing. Using the detection's searching results as feedback reference, the Extended Kalman Filter (EKF) can modify the shifting between the GNSS signal and the true waypoints of the mowing robot. Three cruising demonstrations are presented to show the mapping and optimizing results. Different cases of demonstration represent different situations and potential issues.